C4 Technical Services
Sr. Data Scientist / Machine Learning Engineer
C4 Technical Services, New York, New York, us, 10261
Sr. Data Scientist/Machine Learning Engineer
Start Date: December 1st or January (manager does not want anyone to start in mid-December due to the holidays) Duration: 6 months with the opportunity to extend Location: Washington, D.C., New York is preferred so a candidate may come onsite as needed, but remote is an option for the right candidate Reason for opening: backfill Interview Process: Three rounds of Zoom interviews We are looking for a
hands-on Data Scientist / Machine Learning Engineer
with a strong technical background and deep experience in working with large datasets and advanced modeling techniques. This is an
individual contributor
role within a small team (3-4 people), focused on
research, development, and implementation of machine learning algorithms
to support various business initiatives.
Key Qualifications
Strong technical and programming skills;
Python
and
SQL
required,
R
is a plus. Experience with
advanced machine learning techniques , such as:
Causal ML Forecasting LSTM / Neural Networks Transformers / LLaMA / other large language models Not just basic models like propensity scoring
Solid experience (5+ years) working with
large datasets
and building production-level models. Strong experience with
hands-on coding ; this is
not
a strategic or managerial role. Comfortable with
independent research , staying current on machine learning trends and tools. Should be able to
evaluate and assemble libraries/codebases
into usable solutions for the team. Experience
migrating platforms
(e.g., to Databricks and Salesforce) and improving dashboards and visualizations. Familiarity with
Metalearners
or willingness to research and work with them. Must have a degree in a
quantitative field : Data Science, Engineering, Statistics, Mathematics, etc.
MBAs or Business Analytics degrees are not a fit
for this role.
Client/stakeholder management is
not the primary focus . Interview Tips
Ask candidates to
walk through a machine learning algorithm end-to-end
as applied to a real business problem.
How did they choose the algorithm? Did they utilize forums, libraries, or research? How did they evaluate its effectiveness (both technically and in business terms)?
How you'll make an impact:
You will build machine learning models to answer key business questions impacting strategy and marketing spend allocation You will perform feature engineering and contribute to our feature store You will lead dashboard development and lead development of enhanced visualization tools What you'll do:
Build advanced machine learning models to inform marketing tactics (examples: adaptive clustering, reinforcement learning, regression modeling, price elasticity modeling etc.) Execute sophisticated quantitative analyses and descriptive modeling to answer key business questions to shape business strategy. Use enhanced python based graphical visualizations to deliver key insights to leadership. Research and implement novel modeling techniques to solve complex business problems. Develop analytics databases & lead development and maintenance of automated dashboards for AB experiment results. Develop complex SQL queries combining data from a wide variety of sources in preparation of feature engineering or to perform analysis of business questions. Measure incrementality of paid media campaigns using matched market testing. What you'll need:
Bachelor's or Master's degree in data science, computer science, statistics, engineering, or related quantitative field. Strong prior experience in data science, business & statistical analytics. 7+ years of experience in related field. Strong background in SQL & Python, R is a plus. Experience in machine learning algorithms & libraries. Examples: CausalML/EconML, TensorFlow, PyTorch, Keras, sk-learn, seaborn, LSTM, RNN. Experience with visualization tools. Familiarity with Databricks platform is a plus. Willingness to take initiative and to follow through on projects. Excellent time management skills with the ability to prioritize and multi-task, and work under shifting deadlines in a fast-paced environment. Must have legal right to work in the U.S.
Start Date: December 1st or January (manager does not want anyone to start in mid-December due to the holidays) Duration: 6 months with the opportunity to extend Location: Washington, D.C., New York is preferred so a candidate may come onsite as needed, but remote is an option for the right candidate Reason for opening: backfill Interview Process: Three rounds of Zoom interviews We are looking for a
hands-on Data Scientist / Machine Learning Engineer
with a strong technical background and deep experience in working with large datasets and advanced modeling techniques. This is an
individual contributor
role within a small team (3-4 people), focused on
research, development, and implementation of machine learning algorithms
to support various business initiatives.
Key Qualifications
Strong technical and programming skills;
Python
and
SQL
required,
R
is a plus. Experience with
advanced machine learning techniques , such as:
Causal ML Forecasting LSTM / Neural Networks Transformers / LLaMA / other large language models Not just basic models like propensity scoring
Solid experience (5+ years) working with
large datasets
and building production-level models. Strong experience with
hands-on coding ; this is
not
a strategic or managerial role. Comfortable with
independent research , staying current on machine learning trends and tools. Should be able to
evaluate and assemble libraries/codebases
into usable solutions for the team. Experience
migrating platforms
(e.g., to Databricks and Salesforce) and improving dashboards and visualizations. Familiarity with
Metalearners
or willingness to research and work with them. Must have a degree in a
quantitative field : Data Science, Engineering, Statistics, Mathematics, etc.
MBAs or Business Analytics degrees are not a fit
for this role.
Client/stakeholder management is
not the primary focus . Interview Tips
Ask candidates to
walk through a machine learning algorithm end-to-end
as applied to a real business problem.
How did they choose the algorithm? Did they utilize forums, libraries, or research? How did they evaluate its effectiveness (both technically and in business terms)?
How you'll make an impact:
You will build machine learning models to answer key business questions impacting strategy and marketing spend allocation You will perform feature engineering and contribute to our feature store You will lead dashboard development and lead development of enhanced visualization tools What you'll do:
Build advanced machine learning models to inform marketing tactics (examples: adaptive clustering, reinforcement learning, regression modeling, price elasticity modeling etc.) Execute sophisticated quantitative analyses and descriptive modeling to answer key business questions to shape business strategy. Use enhanced python based graphical visualizations to deliver key insights to leadership. Research and implement novel modeling techniques to solve complex business problems. Develop analytics databases & lead development and maintenance of automated dashboards for AB experiment results. Develop complex SQL queries combining data from a wide variety of sources in preparation of feature engineering or to perform analysis of business questions. Measure incrementality of paid media campaigns using matched market testing. What you'll need:
Bachelor's or Master's degree in data science, computer science, statistics, engineering, or related quantitative field. Strong prior experience in data science, business & statistical analytics. 7+ years of experience in related field. Strong background in SQL & Python, R is a plus. Experience in machine learning algorithms & libraries. Examples: CausalML/EconML, TensorFlow, PyTorch, Keras, sk-learn, seaborn, LSTM, RNN. Experience with visualization tools. Familiarity with Databricks platform is a plus. Willingness to take initiative and to follow through on projects. Excellent time management skills with the ability to prioritize and multi-task, and work under shifting deadlines in a fast-paced environment. Must have legal right to work in the U.S.